Overview

Dataset statistics

Number of variables16
Number of observations450782
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory120.3 MiB
Average record size in memory279.9 B

Variable types

Categorical4
Numeric12

Warnings

name has a high cardinality: 386809 distinct values High cardinality
artists has a high cardinality: 105623 distinct values High cardinality
name is uniformly distributed Uniform
instrumentalness has 158692 (35.2%) zeros Zeros
popularity has 38633 (8.6%) zeros Zeros
key has 57467 (12.7%) zeros Zeros

Reproduction

Analysis started2021-04-26 21:04:27.019329
Analysis finished2021-04-26 21:06:48.127791
Duration2 minutes and 21.11 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct386809
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size36.5 MiB
Intro
 
80
Summertime
 
72
Home
 
55
White Christmas
 
54
2000 Years
 
53
Other values (386804)
450468 

Length

Max length529
Median length16
Mean length20.61142637
Min length1

Characters and Unicode

Total characters9291260
Distinct characters4450
Distinct categories23 ?
Distinct scripts17 ?
Distinct blocks33 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356400 ?
Unique (%)79.1%

Sample

1st row當你走到無力時 (Bonus Song)
2nd rowSTARGAZING
3rd rowAketo vs. Tunisiano
4th rowKarev Yom
5th rowFrisk Me Down
ValueCountFrequency (%)
Intro80
 
< 0.1%
Summertime72
 
< 0.1%
Home55
 
< 0.1%
White Christmas54
 
< 0.1%
2000 Years53
 
< 0.1%
Stay52
 
< 0.1%
Forever51
 
< 0.1%
You47
 
< 0.1%
Winter Wonderland47
 
< 0.1%
Tonight47
 
< 0.1%
Other values (386799)450224
99.9%
2021-04-26T15:06:53.427344image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
96074
 
5.5%
the31799
 
1.8%
in16621
 
1.0%
a16545
 
1.0%
i14578
 
0.8%
you13255
 
0.8%
de13120
 
0.8%
me11846
 
0.7%
of11610
 
0.7%
no10722
 
0.6%
Other values (210257)1500500
86.4%

Most occurring characters

ValueCountFrequency (%)
1285888
 
13.8%
e768420
 
8.3%
a639096
 
6.9%
o484139
 
5.2%
i473919
 
5.1%
n429228
 
4.6%
r403262
 
4.3%
t349897
 
3.8%
l294785
 
3.2%
s283333
 
3.0%
Other values (4440)3879293
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5846729
62.9%
Uppercase Letter1347837
 
14.5%
Space Separator1285888
 
13.8%
Decimal Number227813
 
2.5%
Other Letter214953
 
2.3%
Other Punctuation181034
 
1.9%
Dash Punctuation93850
 
1.0%
Close Punctuation38335
 
0.4%
Open Punctuation38289
 
0.4%
Nonspacing Mark12045
 
0.1%
Other values (13)4487
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
י7481
 
3.5%
ו5864
 
2.7%
ה5127
 
2.4%
ל4622
 
2.2%
א3680
 
1.7%
ר3491
 
1.6%
3284
 
1.5%
3281
 
1.5%
ב3127
 
1.5%
2990
 
1.4%
Other values (3889)172006
80.0%
ValueCountFrequency (%)
e768420
13.1%
a639096
10.9%
o484139
 
8.3%
i473919
 
8.1%
n429228
 
7.3%
r403262
 
6.9%
t349897
 
6.0%
l294785
 
5.0%
s283333
 
4.8%
u212683
 
3.6%
Other values (189)1507967
25.8%
ValueCountFrequency (%)
S105295
 
7.8%
T103348
 
7.7%
M103279
 
7.7%
A87309
 
6.5%
L74939
 
5.6%
D70726
 
5.2%
B66753
 
5.0%
C66413
 
4.9%
R63729
 
4.7%
I63332
 
4.7%
Other values (146)542714
40.3%
ValueCountFrequency (%)
2571
21.3%
2353
19.5%
2075
17.2%
1299
10.8%
915
 
7.6%
599
 
5.0%
489
 
4.1%
439
 
3.6%
376
 
3.1%
267
 
2.2%
Other values (25)662
 
5.5%
ValueCountFrequency (%)
.48209
26.6%
,38511
21.3%
'33960
18.8%
:19914
11.0%
"13872
 
7.7%
/10052
 
5.6%
&5204
 
2.9%
!4116
 
2.3%
?3427
 
1.9%
;1214
 
0.7%
Other values (21)2555
 
1.4%
ValueCountFrequency (%)
°42
32.8%
25
19.5%
18
14.1%
8
 
6.2%
6
 
4.7%
®5
 
3.9%
3
 
2.3%
3
 
2.3%
2
 
1.6%
¦2
 
1.6%
Other values (11)14
 
10.9%
ValueCountFrequency (%)
~290
38.2%
+214
28.2%
|91
 
12.0%
>44
 
5.8%
=44
 
5.8%
<36
 
4.7%
×9
 
1.2%
8
 
1.1%
6
 
0.8%
4
 
0.5%
Other values (11)14
 
1.8%
ValueCountFrequency (%)
049635
21.8%
143294
19.0%
240487
17.8%
917194
 
7.5%
316649
 
7.3%
413820
 
6.1%
512954
 
5.7%
611377
 
5.0%
811221
 
4.9%
711177
 
4.9%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
(36008
94.0%
[1970
 
5.1%
154
 
0.4%
124
 
0.3%
9
 
< 0.1%
9
 
< 0.1%
7
 
< 0.1%
4
 
< 0.1%
{3
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
)36065
94.1%
]1967
 
5.1%
154
 
0.4%
124
 
0.3%
9
 
< 0.1%
7
 
< 0.1%
4
 
< 0.1%
}3
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
2029
95.0%
59
 
2.8%
38
 
1.8%
3
 
0.1%
ˈ2
 
0.1%
2
 
0.1%
ˇ1
 
< 0.1%
ˋ1
 
< 0.1%
ʻ1
 
< 0.1%
ValueCountFrequency (%)
-93438
99.6%
269
 
0.3%
101
 
0.1%
14
 
< 0.1%
14
 
< 0.1%
14
 
< 0.1%
ValueCountFrequency (%)
´139
70.6%
`54
 
27.4%
^2
 
1.0%
΄1
 
0.5%
˙1
 
0.5%
ValueCountFrequency (%)
$167
93.3%
5
 
2.8%
£3
 
1.7%
¥3
 
1.7%
¢1
 
0.6%
ValueCountFrequency (%)
3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
ValueCountFrequency (%)
„1
20.0%
“1
20.0%
’1
20.0%
†1
20.0%
‚1
20.0%
ValueCountFrequency (%)
7
46.7%
5
33.3%
2
 
13.3%
1
 
6.7%
ValueCountFrequency (%)
1
25.0%
³1
25.0%
¹1
25.0%
²1
25.0%
ValueCountFrequency (%)
585
77.2%
132
 
17.4%
»41
 
5.4%
ValueCountFrequency (%)
122
64.6%
«41
 
21.7%
26
 
13.8%
ValueCountFrequency (%)
7
50.0%
6
42.9%
1
 
7.1%
ValueCountFrequency (%)
_92
97.9%
2
 
2.1%
ValueCountFrequency (%)
1285888
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7065090
76.0%
Common1869548
 
20.1%
Cyrillic121157
 
1.3%
Han73615
 
0.8%
Hebrew59968
 
0.6%
Thai54921
 
0.6%
Katakana17703
 
0.2%
Hiragana17650
 
0.2%
Greek8396
 
0.1%
Arabic1819
 
< 0.1%
Other values (7)1393
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
2040
 
2.8%
1477
 
2.0%
1413
 
1.9%
1337
 
1.8%
1005
 
1.4%
971
 
1.3%
851
 
1.2%
791
 
1.1%
677
 
0.9%
676
 
0.9%
Other values (3254)62377
84.7%
ValueCountFrequency (%)
22
 
2.3%
20
 
2.1%
19
 
2.0%
18
 
1.9%
17
 
1.8%
17
 
1.8%
17
 
1.8%
15
 
1.6%
14
 
1.5%
14
 
1.5%
Other values (291)780
81.8%
ValueCountFrequency (%)
e768420
 
10.9%
a639096
 
9.0%
o484139
 
6.9%
i473919
 
6.7%
n429228
 
6.1%
r403262
 
5.7%
t349897
 
5.0%
l294785
 
4.2%
s283333
 
4.0%
u212683
 
3.0%
Other values (208)2726328
38.6%
ValueCountFrequency (%)
1285888
68.8%
-93438
 
5.0%
049635
 
2.7%
.48209
 
2.6%
143294
 
2.3%
240487
 
2.2%
,38511
 
2.1%
)36065
 
1.9%
(36008
 
1.9%
'33960
 
1.8%
Other values (130)164053
 
8.8%
ValueCountFrequency (%)
1477
 
8.3%
917
 
5.2%
778
 
4.4%
740
 
4.2%
705
 
4.0%
653
 
3.7%
538
 
3.0%
468
 
2.6%
423
 
2.4%
421
 
2.4%
Other values (73)10583
59.8%
ValueCountFrequency (%)
2372
 
13.4%
1241
 
7.0%
835
 
4.7%
667
 
3.8%
648
 
3.7%
524
 
3.0%
515
 
2.9%
513
 
2.9%
495
 
2.8%
454
 
2.6%
Other values (69)9386
53.2%
ValueCountFrequency (%)
а13374
 
11.0%
о9608
 
7.9%
е8368
 
6.9%
т7927
 
6.5%
н6205
 
5.1%
и6197
 
5.1%
с6027
 
5.0%
р5788
 
4.8%
л4857
 
4.0%
ь4679
 
3.9%
Other values (61)48127
39.7%
ValueCountFrequency (%)
3284
 
6.0%
3281
 
6.0%
2990
 
5.4%
2594
 
4.7%
2579
 
4.7%
2571
 
4.7%
2463
 
4.5%
2353
 
4.3%
2168
 
3.9%
2075
 
3.8%
Other values (58)28563
52.0%
ValueCountFrequency (%)
α882
 
10.5%
ο609
 
7.3%
ι605
 
7.2%
τ468
 
5.6%
ν459
 
5.5%
ρ395
 
4.7%
ε349
 
4.2%
λ322
 
3.8%
μ321
 
3.8%
ά320
 
3.8%
Other values (55)3666
43.7%
ValueCountFrequency (%)
ا276
15.2%
ي192
 
10.6%
ل191
 
10.5%
ب109
 
6.0%
ن101
 
5.6%
م94
 
5.2%
و91
 
5.0%
ر75
 
4.1%
ح61
 
3.4%
ع59
 
3.2%
Other values (29)570
31.3%
ValueCountFrequency (%)
י7481
12.5%
ו5864
 
9.8%
ה5127
 
8.5%
ל4622
 
7.7%
א3680
 
6.1%
ר3491
 
5.8%
ב3127
 
5.2%
ת2893
 
4.8%
ש2805
 
4.7%
מ2779
 
4.6%
Other values (20)18099
30.2%
ValueCountFrequency (%)
4
 
10.5%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (20)20
52.6%
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18)20
47.6%
ValueCountFrequency (%)
129
39.6%
́75
23.0%
33
 
10.1%
̃21
 
6.4%
̈21
 
6.4%
̊14
 
4.3%
̆10
 
3.1%
̧10
 
3.1%
̂8
 
2.5%
̀3
 
0.9%
Other values (2)2
 
0.6%
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
ValueCountFrequency (%)
3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8832905
95.1%
Cyrillic121157
 
1.3%
None105687
 
1.1%
CJK73577
 
0.8%
Hebrew59968
 
0.6%
Thai54921
 
0.6%
Katakana20761
 
0.2%
Hiragana17812
 
0.2%
Arabic1819
 
< 0.1%
Punctuation1252
 
< 0.1%
Other values (23)1401
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2040
 
2.8%
1477
 
2.0%
1413
 
1.9%
1337
 
1.8%
1005
 
1.4%
971
 
1.3%
851
 
1.2%
791
 
1.1%
677
 
0.9%
676
 
0.9%
Other values (3253)62339
84.7%
ValueCountFrequency (%)
1285888
 
14.6%
e768420
 
8.7%
a639096
 
7.2%
o484139
 
5.5%
i473919
 
5.4%
n429228
 
4.9%
r403262
 
4.6%
t349897
 
4.0%
l294785
 
3.3%
s283333
 
3.2%
Other values (85)3420938
38.7%
ValueCountFrequency (%)
é8851
 
8.4%
ä8474
 
8.0%
á8412
 
8.0%
í6456
 
6.1%
ó6247
 
5.9%
ö5384
 
5.1%
ı5056
 
4.8%
ü4821
 
4.6%
å3265
 
3.1%
ñ2561
 
2.4%
Other values (242)46160
43.7%
ValueCountFrequency (%)
а13374
 
11.0%
о9608
 
7.9%
е8368
 
6.9%
т7927
 
6.5%
н6205
 
5.1%
и6197
 
5.1%
с6027
 
5.0%
р5788
 
4.8%
л4857
 
4.0%
ь4679
 
3.9%
Other values (61)48127
39.7%
ValueCountFrequency (%)
2372
 
13.3%
1241
 
7.0%
835
 
4.7%
667
 
3.7%
648
 
3.6%
524
 
2.9%
515
 
2.9%
513
 
2.9%
495
 
2.8%
454
 
2.5%
Other values (71)9548
53.6%
ValueCountFrequency (%)
3284
 
6.0%
3281
 
6.0%
2990
 
5.4%
2594
 
4.7%
2579
 
4.7%
2571
 
4.7%
2463
 
4.5%
2353
 
4.3%
2168
 
3.9%
2075
 
3.8%
Other values (58)28563
52.0%
ValueCountFrequency (%)
י7481
12.5%
ו5864
 
9.8%
ה5127
 
8.5%
ל4622
 
7.7%
א3680
 
6.1%
ר3491
 
5.8%
ב3127
 
5.2%
ת2893
 
4.8%
ש2805
 
4.7%
מ2779
 
4.6%
Other values (20)18099
30.2%
ValueCountFrequency (%)
2029
 
9.8%
1477
 
7.1%
1029
 
5.0%
917
 
4.4%
778
 
3.7%
740
 
3.6%
705
 
3.4%
653
 
3.1%
538
 
2.6%
468
 
2.3%
Other values (75)11427
55.0%
ValueCountFrequency (%)
8
100.0%
ValueCountFrequency (%)
585
46.7%
208
 
16.6%
132
 
10.5%
122
 
9.7%
101
 
8.1%
26
 
2.1%
14
 
1.1%
14
 
1.1%
14
 
1.1%
9
 
0.7%
Other values (7)27
 
2.2%
ValueCountFrequency (%)
4
 
10.5%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (20)20
52.6%
ValueCountFrequency (%)
22
 
2.4%
20
 
2.2%
19
 
2.1%
18
 
2.0%
17
 
1.9%
17
 
1.9%
17
 
1.9%
15
 
1.7%
14
 
1.6%
14
 
1.6%
Other values (268)726
80.8%
ValueCountFrequency (%)
25
42.4%
18
30.5%
6
 
10.2%
2
 
3.4%
2
 
3.4%
1
 
1.7%
1
 
1.7%
1
 
1.7%
1
 
1.7%
1
 
1.7%
ValueCountFrequency (%)
ˈ2
33.3%
ˇ1
16.7%
ˋ1
16.7%
ʻ1
16.7%
˙1
16.7%
ValueCountFrequency (%)
ə5
71.4%
ɪ2
 
28.6%
ValueCountFrequency (%)
́75
46.0%
̃21
 
12.9%
̈21
 
12.9%
̊14
 
8.6%
̆10
 
6.1%
̧10
 
6.1%
̂8
 
4.9%
̀3
 
1.8%
̦1
 
0.6%
ValueCountFrequency (%)
7
46.7%
5
33.3%
2
 
13.3%
1
 
6.7%
ValueCountFrequency (%)
ا276
15.2%
ي192
 
10.6%
ل191
 
10.5%
ب109
 
6.0%
ن101
 
5.6%
م94
 
5.2%
و91
 
5.0%
ر75
 
4.1%
ح61
 
3.4%
ع59
 
3.2%
Other values (29)570
31.3%
ValueCountFrequency (%)
8
80.0%
2
 
20.0%
ValueCountFrequency (%)
3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
6
 
11.1%
5
 
9.3%
5
 
9.3%
5
 
9.3%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
Other values (13)16
29.6%
ValueCountFrequency (%)
6
46.2%
2
 
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
ValueCountFrequency (%)
3
15.0%
ế3
15.0%
2
10.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (4)4
20.0%
ValueCountFrequency (%)
3
33.3%
2
22.2%
2
22.2%
1
 
11.1%
1
 
11.1%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
10
100.0%
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (18)20
47.6%
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
2
100.0%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
1.0
297285 
0.0
153497 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1352346
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.0297285
65.9%
0.0153497
34.1%
2021-04-26T15:06:54.443180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-26T15:06:54.628347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0297285
65.9%
0.0153497
34.1%

Most occurring characters

ValueCountFrequency (%)
0604279
44.7%
.450782
33.3%
1297285
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number901564
66.7%
Other Punctuation450782
33.3%

Most frequent character per category

ValueCountFrequency (%)
0604279
67.0%
1297285
33.0%
ValueCountFrequency (%)
.450782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1352346
100.0%

Most frequent character per script

ValueCountFrequency (%)
0604279
44.7%
.450782
33.3%
1297285
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1352346
100.0%

Most frequent character per block

ValueCountFrequency (%)
0604279
44.7%
.450782
33.3%
1297285
22.0%

acousticness
Real number (ℝ≥0)

Distinct5526
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4509377866
Minimum0
Maximum0.996
Zeros43
Zeros (%)< 0.1%
Memory size3.4 MiB
2021-04-26T15:06:54.872803image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0017
Q10.0966
median0.423
Q30.788
95-th percentile0.984
Maximum0.996
Range0.996
Interquartile range (IQR)0.6914

Descriptive statistics

Standard deviation0.34988258
Coefficient of variation (CV)0.7758998922
Kurtosis-1.471858827
Mean0.4509377866
Median Absolute Deviation (MAD)0.342
Skewness0.1481166267
Sum203274.6373
Variance0.1224178198
MonotocityNot monotonic
2021-04-26T15:06:55.307737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9953599
 
0.8%
0.9942844
 
0.6%
0.9932296
 
0.5%
0.9922034
 
0.5%
0.9911798
 
0.4%
0.991652
 
0.4%
0.9891541
 
0.3%
0.9881329
 
0.3%
0.9871270
 
0.3%
0.9961216
 
0.3%
Other values (5516)431203
95.7%
ValueCountFrequency (%)
043
< 0.1%
1 × 1061
 
< 0.1%
1.01 × 1064
 
< 0.1%
1.02 × 1061
 
< 0.1%
1.03 × 1062
 
< 0.1%
1.04 × 1062
 
< 0.1%
1.05 × 1063
 
< 0.1%
1.06 × 1062
 
< 0.1%
1.07 × 1061
 
< 0.1%
1.08 × 1061
 
< 0.1%
ValueCountFrequency (%)
0.9961216
 
0.3%
0.9953599
0.8%
0.9942844
0.6%
0.9932296
0.5%
0.9922034
0.5%
0.9911798
0.4%
0.991652
0.4%
0.9891541
0.3%
0.9881329
 
0.3%
0.9871270
 
0.3%

danceability
Real number (ℝ≥0)

Distinct1392
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5631216677
Minimum0
Maximum0.991
Zeros262
Zeros (%)0.1%
Memory size3.4 MiB
2021-04-26T15:06:55.671396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.263
Q10.452
median0.577
Q30.687
95-th percentile0.816
Maximum0.991
Range0.991
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.1675811806
Coefficient of variation (CV)0.2975932027
Kurtosis-0.2786226144
Mean0.5631216677
Median Absolute Deviation (MAD)0.117
Skewness-0.3384426525
Sum253845.1116
Variance0.02808345209
MonotocityNot monotonic
2021-04-26T15:06:56.057379image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6371185
 
0.3%
0.621137
 
0.3%
0.6321125
 
0.2%
0.6291120
 
0.2%
0.631113
 
0.2%
0.611109
 
0.2%
0.6231098
 
0.2%
0.6161096
 
0.2%
0.6211093
 
0.2%
0.5781093
 
0.2%
Other values (1382)439613
97.5%
ValueCountFrequency (%)
0262
0.1%
0.05321
 
< 0.1%
0.05461
 
< 0.1%
0.05511
 
< 0.1%
0.05591
 
< 0.1%
0.05621
 
< 0.1%
0.05692
 
< 0.1%
0.0571
 
< 0.1%
0.05721
 
< 0.1%
0.05742
 
< 0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.9883
< 0.1%
0.9872
 
< 0.1%
0.9863
< 0.1%
0.9854
< 0.1%
0.9844
< 0.1%
0.9832
 
< 0.1%
0.9823
< 0.1%
0.9811
 
< 0.1%
0.987
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct110491
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229072.6134
Minimum4000
Maximum5621218
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-04-26T15:06:56.447947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile95680.65
Q1173160
median213560
Q3263120
95-th percentile384077.45
Maximum5621218
Range5617218
Interquartile range (IQR)89960

Descriptive statistics

Standard deviation128741.3812
Coefficient of variation (CV)0.5620112299
Kurtosis236.0939293
Mean229072.6134
Median Absolute Deviation (MAD)44467
Skewness10.27404755
Sum1.032618108 × 1011
Variance1.657434323 × 1010
MonotocityNot monotonic
2021-04-26T15:06:56.739607image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000172
 
< 0.1%
192000164
 
< 0.1%
180000158
 
< 0.1%
216000145
 
< 0.1%
200000136
 
< 0.1%
210000132
 
< 0.1%
198000132
 
< 0.1%
208000130
 
< 0.1%
184000129
 
< 0.1%
204000122
 
< 0.1%
Other values (110481)449362
99.7%
ValueCountFrequency (%)
40001
< 0.1%
49371
< 0.1%
59911
< 0.1%
63601
< 0.1%
63732
< 0.1%
85941
< 0.1%
88531
< 0.1%
96801
< 0.1%
98132
< 0.1%
120001
< 0.1%
ValueCountFrequency (%)
56212181
< 0.1%
50421851
< 0.1%
48643331
< 0.1%
48001181
< 0.1%
47972581
< 0.1%
47925871
< 0.1%
47866721
< 0.1%
47755181
< 0.1%
47566221
< 0.1%
47374581
< 0.1%

energy
Real number (ℝ≥0)

Distinct2638
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5383459661
Minimum0
Maximum1
Zeros16
Zeros (%)< 0.1%
Memory size3.4 MiB
2021-04-26T15:06:57.127448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.115
Q10.337
median0.545
Q30.746
95-th percentile0.931
Maximum1
Range1
Interquartile range (IQR)0.409

Descriptive statistics

Standard deviation0.2536301486
Coefficient of variation (CV)0.4711285393
Kurtosis-0.9793981571
Mean0.5383459661
Median Absolute Deviation (MAD)0.204
Skewness-0.1192714974
Sum242676.6713
Variance0.06432825229
MonotocityNot monotonic
2021-04-26T15:06:57.456738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.499654
 
0.1%
0.526647
 
0.1%
0.53642
 
0.1%
0.657636
 
0.1%
0.448635
 
0.1%
0.534633
 
0.1%
0.675631
 
0.1%
0.726631
 
0.1%
0.716629
 
0.1%
0.482624
 
0.1%
Other values (2628)444420
98.6%
ValueCountFrequency (%)
016
< 0.1%
1.97 × 1052
 
< 0.1%
1.98 × 1051
 
< 0.1%
1.99 × 1052
 
< 0.1%
2 × 1054
 
< 0.1%
2.01 × 1059
 
< 0.1%
2.02 × 10511
< 0.1%
2.03 × 10527
< 0.1%
2.8 × 1051
 
< 0.1%
3.05 × 1051
 
< 0.1%
ValueCountFrequency (%)
146
 
< 0.1%
0.999160
< 0.1%
0.998167
< 0.1%
0.997195
< 0.1%
0.996197
< 0.1%
0.995233
0.1%
0.994203
< 0.1%
0.993198
< 0.1%
0.992193
< 0.1%
0.991242
0.1%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5797
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.115369943
Minimum0
Maximum1
Zeros158692
Zeros (%)35.2%
Memory size3.4 MiB
2021-04-26T15:06:57.803322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.43 × 105
Q30.0103
95-th percentile0.877
Maximum1
Range1
Interquartile range (IQR)0.0103

Descriptive statistics

Standard deviation0.2690694461
Coefficient of variation (CV)2.332231769
Kurtosis3.409495255
Mean0.115369943
Median Absolute Deviation (MAD)2.43 × 105
Skewness2.241608319
Sum52006.69363
Variance0.07239836684
MonotocityNot monotonic
2021-04-26T15:06:58.191003image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0158692
35.2%
0.916326
 
0.1%
0.917313
 
0.1%
0.905311
 
0.1%
0.911306
 
0.1%
0.894306
 
0.1%
0.906306
 
0.1%
0.909304
 
0.1%
0.924303
 
0.1%
0.904303
 
0.1%
Other values (5787)289312
64.2%
ValueCountFrequency (%)
0158692
35.2%
1 × 106103
 
< 0.1%
1.01 × 106196
 
< 0.1%
1.02 × 106201
 
< 0.1%
1.03 × 106201
 
< 0.1%
1.04 × 106213
 
< 0.1%
1.05 × 106190
 
< 0.1%
1.06 × 106178
 
< 0.1%
1.07 × 106211
 
< 0.1%
1.08 × 106175
 
< 0.1%
ValueCountFrequency (%)
118
< 0.1%
0.99916
< 0.1%
0.9989
< 0.1%
0.9978
 
< 0.1%
0.99612
< 0.1%
0.99514
< 0.1%
0.99419
< 0.1%
0.99316
< 0.1%
0.99221
< 0.1%
0.99117
< 0.1%

liveness
Real number (ℝ≥0)

Distinct1889
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2150238325
Minimum0
Maximum1
Zeros25
Zeros (%)< 0.1%
Memory size3.4 MiB
2021-04-26T15:06:58.556166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0592
Q10.0987
median0.139
Q30.279
95-th percentile0.655
Maximum1
Range1
Interquartile range (IQR)0.1803

Descriptive statistics

Standard deviation0.1850502519
Coefficient of variation (CV)0.8606034492
Kurtosis4.205854725
Mean0.2150238325
Median Absolute Deviation (MAD)0.058
Skewness2.03064211
Sum96928.87325
Variance0.03424359572
MonotocityNot monotonic
2021-04-26T15:06:58.941603image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1114287
 
1.0%
0.114109
 
0.9%
0.1084036
 
0.9%
0.1093946
 
0.9%
0.1073810
 
0.8%
0.1123721
 
0.8%
0.1063713
 
0.8%
0.1053569
 
0.8%
0.1043500
 
0.8%
0.1033454
 
0.8%
Other values (1879)412637
91.5%
ValueCountFrequency (%)
025
< 0.1%
0.005721
 
< 0.1%
0.008381
 
< 0.1%
0.009861
 
< 0.1%
0.009891
 
< 0.1%
0.01011
 
< 0.1%
0.01031
 
< 0.1%
0.01082
 
< 0.1%
0.01112
 
< 0.1%
0.01122
 
< 0.1%
ValueCountFrequency (%)
13
 
< 0.1%
0.9993
 
< 0.1%
0.9984
 
< 0.1%
0.9978
 
< 0.1%
0.99611
 
< 0.1%
0.99516
 
< 0.1%
0.99417
< 0.1%
0.99316
 
< 0.1%
0.99232
< 0.1%
0.99140
< 0.1%

loudness
Real number (ℝ)

Distinct30462
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.31613156
Minimum-60
Maximum5.376
Zeros0
Zeros (%)0.0%
Memory size3.4 MiB
2021-04-26T15:06:59.324840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-20.146
Q1-13.04275
median-9.317
Q3-6.522
95-th percentile-3.932
Maximum5.376
Range65.376
Interquartile range (IQR)6.52075

Descriptive statistics

Standard deviation5.170084796
Coefficient of variation (CV)-0.5011650701
Kurtosis2.44424799
Mean-10.31613156
Median Absolute Deviation (MAD)3.142
Skewness-1.210691337
Sum-4650326.416
Variance26.7297768
MonotocityNot monotonic
2021-04-26T15:06:59.648236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.1363
 
< 0.1%
-7.01363
 
< 0.1%
-7.01662
 
< 0.1%
-6.89662
 
< 0.1%
-7.86661
 
< 0.1%
-6.5961
 
< 0.1%
-6.15860
 
< 0.1%
-7.31560
 
< 0.1%
-6.59660
 
< 0.1%
-6.79559
 
< 0.1%
Other values (30452)450171
99.9%
ValueCountFrequency (%)
-6011
< 0.1%
-57.0931
 
< 0.1%
-551
 
< 0.1%
-54.8371
 
< 0.1%
-54.3761
 
< 0.1%
-53.5981
 
< 0.1%
-52.221
 
< 0.1%
-51.81
 
< 0.1%
-51.1231
 
< 0.1%
-51.081
 
< 0.1%
ValueCountFrequency (%)
5.3761
< 0.1%
4.5841
< 0.1%
4.3621
< 0.1%
3.8551
< 0.1%
3.7441
< 0.1%
3.2731
< 0.1%
3.091
< 0.1%
2.7991
< 0.1%
2.7691
< 0.1%
2.6631
< 0.1%

speechiness
Real number (ℝ≥0)

Distinct1753
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1119874108
Minimum0
Maximum0.971
Zeros262
Zeros (%)0.1%
Memory size3.4 MiB
2021-04-26T15:06:59.988154image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0277
Q10.0342
median0.0449
Q30.08
95-th percentile0.51
Maximum0.971
Range0.971
Interquartile range (IQR)0.0458

Descriptive statistics

Standard deviation0.1917614847
Coefficient of variation (CV)1.712348588
Kurtosis11.33532298
Mean0.1119874108
Median Absolute Deviation (MAD)0.0138
Skewness3.44431331
Sum50481.909
Variance0.03677246702
MonotocityNot monotonic
2021-04-26T15:07:01.027302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03121510
 
0.3%
0.0331501
 
0.3%
0.03111501
 
0.3%
0.03321501
 
0.3%
0.03241495
 
0.3%
0.03181492
 
0.3%
0.03131488
 
0.3%
0.0311486
 
0.3%
0.03191484
 
0.3%
0.03151482
 
0.3%
Other values (1743)435842
96.7%
ValueCountFrequency (%)
0262
0.1%
0.02162
 
< 0.1%
0.02182
 
< 0.1%
0.0222
 
< 0.1%
0.02215
 
< 0.1%
0.02226
 
< 0.1%
0.022314
 
< 0.1%
0.022411
 
< 0.1%
0.022514
 
< 0.1%
0.022613
 
< 0.1%
ValueCountFrequency (%)
0.9713
 
< 0.1%
0.973
 
< 0.1%
0.96921
 
< 0.1%
0.96826
 
< 0.1%
0.96738
 
< 0.1%
0.96682
 
< 0.1%
0.965116
< 0.1%
0.964153
< 0.1%
0.963213
< 0.1%
0.962234
0.1%

tempo
Real number (ℝ≥0)

Distinct122343
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.3177983
Minimum0
Maximum243.759
Zeros262
Zeros (%)0.1%
Memory size3.4 MiB
2021-04-26T15:07:01.460488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.71205
Q195.19425
median117.107
Q3136.434
95-th percentile173.99695
Maximum243.759
Range243.759
Interquartile range (IQR)41.23975

Descriptive statistics

Standard deviation29.89915114
Coefficient of variation (CV)0.2527020581
Kurtosis-0.08350366236
Mean118.3177983
Median Absolute Deviation (MAD)20.868
Skewness0.3995702866
Sum53335533.75
Variance893.9592391
MonotocityNot monotonic
2021-04-26T15:07:01.904109image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0262
 
0.1%
119.99469
 
< 0.1%
127.99767
 
< 0.1%
127.99466
 
< 0.1%
12065
 
< 0.1%
130.00265
 
< 0.1%
120.00164
 
< 0.1%
128.00164
 
< 0.1%
119.99664
 
< 0.1%
119.98564
 
< 0.1%
Other values (122333)449932
99.8%
ValueCountFrequency (%)
0262
0.1%
30.5061
 
< 0.1%
30.9461
 
< 0.1%
31.291
 
< 0.1%
31.691
 
< 0.1%
32.1631
 
< 0.1%
32.2051
 
< 0.1%
32.3321
 
< 0.1%
32.4661
 
< 0.1%
32.4961
 
< 0.1%
ValueCountFrequency (%)
243.7591
< 0.1%
243.5071
< 0.1%
243.3721
< 0.1%
240.7821
< 0.1%
239.9061
< 0.1%
238.8951
< 0.1%
236.1341
< 0.1%
233.0131
< 0.1%
232.8511
< 0.1%
231.2511
< 0.1%

valence
Real number (ℝ≥0)

Distinct1920
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5489413263
Minimum0
Maximum1
Zeros311
Zeros (%)0.1%
Memory size3.4 MiB
2021-04-26T15:07:02.272270image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.117
Q10.343
median0.56
Q30.765
95-th percentile0.945
Maximum1
Range1
Interquartile range (IQR)0.422

Descriptive statistics

Standard deviation0.2580248596
Coefficient of variation (CV)0.4700408719
Kurtosis-1.033162142
Mean0.5489413263
Median Absolute Deviation (MAD)0.211
Skewness-0.1452974027
Sum247452.8689
Variance0.06657682819
MonotocityNot monotonic
2021-04-26T15:07:02.694492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9612028
 
0.4%
0.9621729
 
0.4%
0.9631526
 
0.3%
0.9641386
 
0.3%
0.961227
 
0.3%
0.9651219
 
0.3%
0.9661117
 
0.2%
0.9671029
 
0.2%
0.968883
 
0.2%
0.969704
 
0.2%
Other values (1910)437934
97.1%
ValueCountFrequency (%)
0311
0.1%
1 × 105115
 
< 0.1%
6.41 × 1051
 
< 0.1%
0.0001831
 
< 0.1%
0.0005621
 
< 0.1%
0.0009981
 
< 0.1%
0.001231
 
< 0.1%
0.001281
 
< 0.1%
0.001551
 
< 0.1%
0.001661
 
< 0.1%
ValueCountFrequency (%)
113
< 0.1%
0.9991
 
< 0.1%
0.9975
 
< 0.1%
0.9967
 
< 0.1%
0.9955
 
< 0.1%
0.9949
< 0.1%
0.9935
 
< 0.1%
0.9929
< 0.1%
0.99116
< 0.1%
0.9922
< 0.1%

popularity
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.52036461
Minimum0
Maximum100
Zeros38633
Zeros (%)8.6%
Memory size3.4 MiB
2021-04-26T15:07:03.097403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median27
Q341
95-th percentile59
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.4284811
Coefficient of variation (CV)0.6696307028
Kurtosis-0.6621476293
Mean27.52036461
Median Absolute Deviation (MAD)14
Skewness0.2585567201
Sum12405685
Variance339.6089155
MonotocityNot monotonic
2021-04-26T15:07:03.430378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038633
 
8.6%
239348
 
2.1%
359321
 
2.1%
368982
 
2.0%
228842
 
2.0%
248782
 
1.9%
348689
 
1.9%
278678
 
1.9%
18665
 
1.9%
288481
 
1.9%
Other values (90)332361
73.7%
ValueCountFrequency (%)
038633
8.6%
18665
 
1.9%
26917
 
1.5%
35853
 
1.3%
45469
 
1.2%
55534
 
1.2%
65697
 
1.3%
75852
 
1.3%
86049
 
1.3%
96236
 
1.4%
ValueCountFrequency (%)
1002
 
< 0.1%
991
 
< 0.1%
971
 
< 0.1%
964
 
< 0.1%
951
 
< 0.1%
948
< 0.1%
935
 
< 0.1%
9213
< 0.1%
9110
< 0.1%
9011
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.217357836
Minimum0
Maximum11
Zeros57467
Zeros (%)12.7%
Memory size3.4 MiB
2021-04-26T15:07:03.714576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.522235674
Coefficient of variation (CV)0.6750995014
Kurtosis-1.267855369
Mean5.217357836
Median Absolute Deviation (MAD)3
Skewness0.001070919015
Sum2351891
Variance12.40614414
MonotocityNot monotonic
2021-04-26T15:07:03.941458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
057467
12.7%
756641
12.6%
251246
11.4%
949608
11.0%
540978
9.1%
436661
8.1%
132762
7.3%
1130236
6.7%
1029006
6.4%
825732
5.7%
Other values (2)40445
9.0%
ValueCountFrequency (%)
057467
12.7%
132762
7.3%
251246
11.4%
316346
 
3.6%
436661
8.1%
540978
9.1%
624099
5.3%
756641
12.6%
825732
5.7%
949608
11.0%
ValueCountFrequency (%)
1130236
6.7%
1029006
6.4%
949608
11.0%
825732
5.7%
756641
12.6%
624099
5.3%
540978
9.1%
436661
8.1%
316346
 
3.6%
251246
11.4%

artists
Categorical

HIGH CARDINALITY

Distinct105623
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size35.7 MiB
['Die drei ???']
 
3055
['TKKG Retro-Archiv']
 
1592
['Bibi Blocksberg']
 
1174
['Benjamin Blümchen']
 
1164
['Эрнест Хемингуэй']
 
985
Other values (105618)
442812 

Length

Max length934
Median length17
Mean length21.87595556
Min length5

Characters and Unicode

Total characters9861287
Distinct characters2073
Distinct categories20 ?
Distinct scripts13 ?
Distinct blocks23 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64926 ?
Unique (%)14.4%

Sample

1st row['徐偉賢']
2nd row['Travis Scott']
3rd row['SNIPER']
4th row['Theodore Bikel']
5th row['Katchafire']
ValueCountFrequency (%)
['Die drei ???']3055
 
0.7%
['TKKG Retro-Archiv']1592
 
0.4%
['Bibi Blocksberg']1174
 
0.3%
['Benjamin Blümchen']1164
 
0.3%
['Эрнест Хемингуэй']985
 
0.2%
['Lata Mangeshkar']952
 
0.2%
['Эрих Мария Ремарк']853
 
0.2%
['Francisco Canaro']741
 
0.2%
['Tintin', 'Tomas Bolme', 'Bert-Åke Varg']717
 
0.2%
['Bibi und Tina']715
 
0.2%
Other values (105613)438834
97.3%
2021-04-26T15:07:05.410113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the24246
 
2.0%
17208
 
1.4%
orchestra9784
 
0.8%
de7601
 
0.6%
los7052
 
0.6%
die4175
 
0.3%
la3650
 
0.3%
del3372
 
0.3%
john3215
 
0.3%
drei3083
 
0.3%
Other values (76811)1127984
93.1%

Most occurring characters

ValueCountFrequency (%)
'1167039
 
11.8%
760590
 
7.7%
a681018
 
6.9%
e596425
 
6.0%
i470909
 
4.8%
r453642
 
4.6%
[450836
 
4.6%
]450836
 
4.6%
n445497
 
4.5%
o432011
 
4.4%
Other values (2063)3952484
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5493698
55.7%
Other Punctuation1367679
 
13.9%
Uppercase Letter1257442
 
12.8%
Space Separator760590
 
7.7%
Close Punctuation451680
 
4.6%
Open Punctuation451679
 
4.6%
Other Letter44956
 
0.5%
Decimal Number15724
 
0.2%
Dash Punctuation11823
 
0.1%
Nonspacing Mark4233
 
< 0.1%
Other values (10)1783
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
1464
 
3.3%
1231
 
2.7%
889
 
2.0%
840
 
1.9%
836
 
1.9%
738
 
1.6%
711
 
1.6%
688
 
1.5%
598
 
1.3%
558
 
1.2%
Other values (1655)36403
81.0%
ValueCountFrequency (%)
a681018
12.4%
e596425
10.9%
i470909
 
8.6%
r453642
 
8.3%
n445497
 
8.1%
o432011
 
7.9%
l308886
 
5.6%
s306372
 
5.6%
t257679
 
4.7%
h199479
 
3.6%
Other values (170)1341780
24.4%
ValueCountFrequency (%)
S106385
 
8.5%
M94670
 
7.5%
B88079
 
7.0%
C79167
 
6.3%
T77876
 
6.2%
A77597
 
6.2%
D64906
 
5.2%
L63054
 
5.0%
R61545
 
4.9%
P59255
 
4.7%
Other values (116)484908
38.6%
ValueCountFrequency (%)
'1167039
85.3%
,138049
 
10.1%
.23451
 
1.7%
"13514
 
1.0%
&13132
 
1.0%
?9237
 
0.7%
/1246
 
0.1%
!1107
 
0.1%
:202
 
< 0.1%
179
 
< 0.1%
Other values (17)523
 
< 0.1%
ValueCountFrequency (%)
1098
25.9%
816
19.3%
585
13.8%
407
 
9.6%
356
 
8.4%
280
 
6.6%
231
 
5.5%
200
 
4.7%
92
 
2.2%
77
 
1.8%
Other values (4)91
 
2.1%
ValueCountFrequency (%)
12771
17.6%
22551
16.2%
02263
14.4%
31480
9.4%
41434
9.1%
51204
7.7%
71103
 
7.0%
91088
 
6.9%
8977
 
6.2%
6853
 
5.4%
ValueCountFrequency (%)
+141
77.9%
|24
 
13.3%
=8
 
4.4%
~2
 
1.1%
×2
 
1.1%
1
 
0.6%
1
 
0.6%
<1
 
0.6%
>1
 
0.6%
ValueCountFrequency (%)
18
47.4%
°9
23.7%
5
 
13.2%
®2
 
5.3%
2
 
5.3%
©1
 
2.6%
1
 
2.6%
ValueCountFrequency (%)
´56
70.0%
`15
 
18.8%
^6
 
7.5%
¨1
 
1.2%
1
 
1.2%
¯1
 
1.2%
ValueCountFrequency (%)
$604
97.7%
¥9
 
1.5%
3
 
0.5%
¢1
 
0.2%
£1
 
0.2%
ValueCountFrequency (%)
[450836
99.8%
(828
 
0.2%
9
 
< 0.1%
6
 
< 0.1%
ValueCountFrequency (%)
]450836
99.8%
)830
 
0.2%
9
 
< 0.1%
5
 
< 0.1%
ValueCountFrequency (%)
-11719
99.1%
98
 
0.8%
6
 
0.1%
ValueCountFrequency (%)
«45
77.6%
10
 
17.2%
3
 
5.2%
ValueCountFrequency (%)
141
66.5%
»45
 
21.2%
26
 
12.3%
ValueCountFrequency (%)
538
99.1%
5
 
0.9%
ValueCountFrequency (%)
²3
75.0%
³1
 
25.0%
ValueCountFrequency (%)
760590
100.0%
ValueCountFrequency (%)
_48
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6685998
67.8%
Common3060947
31.0%
Cyrillic57491
 
0.6%
Han23595
 
0.2%
Thai19150
 
0.2%
Greek7654
 
0.1%
Katakana3685
 
< 0.1%
Hebrew1467
 
< 0.1%
Hiragana833
 
< 0.1%
Arabic366
 
< 0.1%
Other values (3)101
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
598
 
2.5%
385
 
1.6%
322
 
1.4%
306
 
1.3%
303
 
1.3%
300
 
1.3%
288
 
1.2%
285
 
1.2%
265
 
1.1%
259
 
1.1%
Other values (1357)20284
86.0%
ValueCountFrequency (%)
a681018
 
10.2%
e596425
 
8.9%
i470909
 
7.0%
r453642
 
6.8%
n445497
 
6.7%
o432011
 
6.5%
l308886
 
4.6%
s306372
 
4.6%
t257679
 
3.9%
h199479
 
3.0%
Other values (165)2534080
37.9%
ValueCountFrequency (%)
'1167039
38.1%
760590
24.8%
[450836
 
14.7%
]450836
 
14.7%
,138049
 
4.5%
.23451
 
0.8%
"13514
 
0.4%
&13132
 
0.4%
-11719
 
0.4%
?9237
 
0.3%
Other values (76)22544
 
0.7%
ValueCountFrequency (%)
307
 
8.3%
281
 
7.6%
256
 
6.9%
251
 
6.8%
195
 
5.3%
190
 
5.2%
187
 
5.1%
169
 
4.6%
107
 
2.9%
99
 
2.7%
Other values (68)1643
44.6%
ValueCountFrequency (%)
р5369
 
9.3%
и4961
 
8.6%
е4802
 
8.4%
а4729
 
8.2%
н4213
 
7.3%
м2490
 
4.3%
о2282
 
4.0%
т2153
 
3.7%
с2030
 
3.5%
Э1851
 
3.2%
Other values (58)22611
39.3%
ValueCountFrequency (%)
1464
 
7.6%
1231
 
6.4%
1098
 
5.7%
889
 
4.6%
840
 
4.4%
836
 
4.4%
816
 
4.3%
738
 
3.9%
711
 
3.7%
688
 
3.6%
Other values (52)9839
51.4%
ValueCountFrequency (%)
48
 
5.8%
46
 
5.5%
44
 
5.3%
43
 
5.2%
43
 
5.2%
35
 
4.2%
32
 
3.8%
28
 
3.4%
28
 
3.4%
26
 
3.1%
Other values (49)460
55.2%
ValueCountFrequency (%)
5
 
5.9%
5
 
5.9%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (47)56
65.9%
ValueCountFrequency (%)
ς679
 
8.9%
α673
 
8.8%
ο493
 
6.4%
ρ439
 
5.7%
τ429
 
5.6%
η398
 
5.2%
ν330
 
4.3%
ι286
 
3.7%
λ275
 
3.6%
κ267
 
3.5%
Other values (46)3385
44.2%
ValueCountFrequency (%)
י247
16.8%
ו151
 
10.3%
ר114
 
7.8%
ה107
 
7.3%
ב95
 
6.5%
נ91
 
6.2%
ל84
 
5.7%
א83
 
5.7%
ן67
 
4.6%
מ45
 
3.1%
Other values (18)383
26.1%
ValueCountFrequency (%)
م39
 
10.7%
ي39
 
10.7%
ل28
 
7.7%
ا27
 
7.4%
ز27
 
7.4%
ف25
 
6.8%
د23
 
6.3%
ر20
 
5.5%
ح19
 
5.2%
و17
 
4.6%
Other values (17)102
27.9%
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
ValueCountFrequency (%)
́2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9677475
98.1%
None76092
 
0.8%
Cyrillic57491
 
0.6%
CJK23584
 
0.2%
Thai19150
 
0.2%
Katakana4402
 
< 0.1%
Hebrew1467
 
< 0.1%
Hiragana834
 
< 0.1%
Arabic366
 
< 0.1%
Punctuation282
 
< 0.1%
Other values (13)144
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
'1167039
 
12.1%
760590
 
7.9%
a681018
 
7.0%
e596425
 
6.2%
i470909
 
4.9%
r453642
 
4.7%
[450836
 
4.7%
]450836
 
4.7%
n445497
 
4.6%
o432011
 
4.5%
Other values (83)3768672
38.9%
ValueCountFrequency (%)
598
 
2.5%
385
 
1.6%
322
 
1.4%
306
 
1.3%
303
 
1.3%
300
 
1.3%
288
 
1.2%
285
 
1.2%
265
 
1.1%
259
 
1.1%
Other values (1354)20273
86.0%
ValueCountFrequency (%)
é10533
 
13.8%
á7540
 
9.9%
ü6075
 
8.0%
ó5167
 
6.8%
í4719
 
6.2%
ö4161
 
5.5%
ı2235
 
2.9%
ä1939
 
2.5%
ç1636
 
2.2%
ú1621
 
2.1%
Other values (194)30466
40.0%
ValueCountFrequency (%)
י247
16.8%
ו151
 
10.3%
ר114
 
7.8%
ה107
 
7.3%
ב95
 
6.5%
נ91
 
6.2%
ל84
 
5.7%
א83
 
5.7%
ן67
 
4.6%
מ45
 
3.1%
Other values (18)383
26.1%
ValueCountFrequency (%)
1464
 
7.6%
1231
 
6.4%
1098
 
5.7%
889
 
4.6%
840
 
4.4%
836
 
4.4%
816
 
4.3%
738
 
3.9%
711
 
3.7%
688
 
3.6%
Other values (52)9839
51.4%
ValueCountFrequency (%)
р5369
 
9.3%
и4961
 
8.6%
е4802
 
8.4%
а4729
 
8.2%
н4213
 
7.3%
м2490
 
4.3%
о2282
 
4.0%
т2153
 
3.7%
с2030
 
3.5%
Э1851
 
3.2%
Other values (58)22611
39.3%
ValueCountFrequency (%)
48
 
5.8%
46
 
5.5%
44
 
5.3%
43
 
5.2%
43
 
5.2%
35
 
4.2%
32
 
3.8%
28
 
3.4%
28
 
3.4%
26
 
3.1%
Other values (50)461
55.3%
ValueCountFrequency (%)
538
 
12.2%
307
 
7.0%
281
 
6.4%
256
 
5.8%
251
 
5.7%
195
 
4.4%
190
 
4.3%
187
 
4.2%
179
 
4.1%
169
 
3.8%
Other values (70)1849
42.0%
ValueCountFrequency (%)
م39
 
10.7%
ي39
 
10.7%
ل28
 
7.7%
ا27
 
7.4%
ز27
 
7.4%
ف25
 
6.8%
د23
 
6.3%
ر20
 
5.5%
ح19
 
5.2%
و17
 
4.6%
Other values (17)102
27.9%
ValueCountFrequency (%)
141
50.0%
98
34.8%
26
 
9.2%
10
 
3.5%
3
 
1.1%
3
 
1.1%
1
 
0.4%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
5
 
5.9%
5
 
5.9%
3
 
3.5%
3
 
3.5%
3
 
3.5%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (47)56
65.9%
ValueCountFrequency (%)
18
90.0%
2
 
10.0%
ValueCountFrequency (%)
5
100.0%
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%
ValueCountFrequency (%)
4
28.6%
3
21.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
ValueCountFrequency (%)
́2
100.0%
ValueCountFrequency (%)
3
100.0%
ValueCountFrequency (%)
𤒹1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 MiB
0.0
425739 
1.0
 
25043

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1352346
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0425739
94.4%
1.025043
 
5.6%
2021-04-26T15:07:05.989284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-26T15:07:06.152713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0425739
94.4%
1.025043
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0876521
64.8%
.450782
33.3%
125043
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number901564
66.7%
Other Punctuation450782
33.3%

Most frequent character per category

ValueCountFrequency (%)
0876521
97.2%
125043
 
2.8%
ValueCountFrequency (%)
.450782
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1352346
100.0%

Most frequent character per script

ValueCountFrequency (%)
0876521
64.8%
.450782
33.3%
125043
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1352346
100.0%

Most frequent character per block

ValueCountFrequency (%)
0876521
64.8%
.450782
33.3%
125043
 
1.9%

Interactions

2021-04-26T15:05:45.740568image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:46.408832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:47.035386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:47.652719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:48.212523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:48.782787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:49.234928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:49.581946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:49.959569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:50.306491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:50.658798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:51.036583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:51.389470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:51.741145image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:52.123421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:52.473787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:52.841602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:53.215983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:53.565081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:53.931524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:54.322617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:54.676872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:55.035560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:55.410967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:56.126642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:56.474329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:56.833123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:57.216932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:57.577051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:57.995391image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:58.387059image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:58.749907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:59.236187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:05:59.607758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:00.026021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:00.535149image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:01.878958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:02.615707image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:03.055066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:03.584296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:04.033793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:04.513787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:05.130806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:06.057824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:07.025892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:07.397957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:07.775437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:08.184294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:08.550142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:08.943739image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:09.324371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:09.681230image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:10.150481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:10.521659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:10.944459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:11.430824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:11.796336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:12.204306image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:12.631087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:12.991677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:13.355444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:13.720729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:14.076395image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:14.906533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:15.262527image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:15.647506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:16.007550image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:16.385966image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:16.752617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:17.176716image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:17.627301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:18.034279image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:18.407576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:18.777409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:19.138554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:19.501925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:19.866573image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:20.274340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:20.666677image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:21.078736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:21.533153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:21.905438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:22.262463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:22.676587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:23.158365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:23.540181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:23.899025image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:24.259392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:24.632730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:25.000400image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:25.422562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:25.791782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:26.135330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:26.524176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:26.889575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:27.244876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:27.605654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:27.962811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:28.313861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:28.662313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:29.016565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:29.363291image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:29.732911image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:30.074870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:30.478464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:30.842558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:31.194416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:31.553340image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:31.911780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:32.260709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:32.619244image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:33.428502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:33.795271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:34.142403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:34.486559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:34.849508image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:35.199317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:35.556632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:35.937110image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:36.280281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:36.685875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:37.049884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:37.394659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:37.775205image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:38.124647image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:38.475446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:38.846643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:39.202797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:39.612995image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:40.009461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:40.370772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-26T15:06:40.760695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-26T15:07:06.326706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-26T15:07:06.818081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-26T15:07:07.253386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-26T15:07:07.725808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-26T15:07:09.009221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-26T15:06:42.605918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-26T15:06:44.148099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

namemodeacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencepopularitykeyartistsexplicit
0當你走到無力時 (Bonus Song)1.00.948000.476254413.00.16100.0000430.0669-13.8890.057591.5990.244029.05.0['徐偉賢']0.0
1STARGAZING1.00.009470.487270715.00.78900.0000040.1890-3.6900.0440150.0070.056479.08.0['Travis Scott']1.0
2Aketo vs. Tunisiano1.00.072900.722244427.00.77300.0000000.0728-4.9270.347090.9050.921038.011.0['SNIPER']0.0
3Karev Yom1.00.956000.578180028.00.01930.0003010.1070-24.5280.0409111.4700.44303.07.0['Theodore Bikel']0.0
4Frisk Me Down0.00.220000.809259067.00.47000.0002310.0479-7.1340.0751133.9570.881026.09.0['Katchafire']0.0
5Swordfishtrombone0.00.940000.912188333.00.23200.0024500.0776-20.4310.0801118.0470.681035.09.0['Tom Waits']0.0
6渺小0.00.829000.209255800.00.17500.0000010.1220-14.1980.031086.7700.10508.04.0['Zhang Yu Sheng']0.0
7The Harvest1.00.936000.588184520.00.16300.0000000.1150-13.4810.034173.3030.559043.07.0['Tyler Childers']0.0
8Gel Aşalım Aşalım / Cimil Horonu1.00.402000.690244878.00.93000.0000000.2180-10.3280.416073.0090.845039.06.0['Cimilli İbo']0.0
9Macumba0.00.090000.640260526.00.76100.0078400.1370-12.1280.0410137.4210.91108.02.0['July Mateo Rasputin']0.0

Last rows

namemodeacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinesstempovalencepopularitykeyartistsexplicit
450772Li Lathaton1.00.985000.388444280.00.2230.0121000.3680-19.9440.050371.8030.3890.02.0['Umm Kulthum']0.0
450773Sona Sona0.00.082800.739361430.00.6760.0000300.3880-10.6470.055899.9880.63329.010.0['Hariharan', 'Srinivasa Murthy', 'Savitha']0.0
450774Show Me - 2007 Remaster1.00.000620.550253893.00.8970.0656000.0621-6.9270.0395139.2990.56235.00.0['Pretenders']0.0
450775Poď So Mnou1.00.407000.730231925.00.8110.0000050.2820-2.1440.0609135.0850.74433.00.0['Kali', 'I.M.T. Smile']0.0
450776Ride ranke1.00.150000.550223813.00.5260.0000000.1900-8.2090.040880.3700.39129.02.0['Finn Kalvik', 'Praha Philharmonic Orchestra']0.0
450777Los Pegaditos de Super Grupo Antillano Nº1: Colé Colé / Niégalo / Enamorada / La Pollera Amarilla / Oye Mi Canto0.00.532000.398583480.00.6240.0000000.1040-10.8040.0676101.7330.8248.05.0['Super Grupo Antillano', 'Marihel']0.0
450778Ramuloo Ramulaa - Duet0.00.415000.663245760.00.9130.0004960.1580-4.6810.1520187.5520.80560.05.0['Anurag Kulkarni', 'Mangli', 'Thaman S']0.0
450779Thing of Beauty1.00.272000.558327000.00.6040.0050500.0745-9.4220.0321127.0320.17537.05.0['Hothouse Flowers']0.0
450780Strike up the Band (Live)0.00.938000.545283320.00.3440.8660000.1900-17.4560.0511129.7130.6279.07.0['Heinz Bigler Quartett']0.0
450781Imidžák1.00.014600.564171147.00.9730.0000000.1930-2.4580.0878100.0070.51718.00.0['Iné Kafe']0.0